wesm

# pylint: disable=W0612

import time

import pandas as pd
import numpy as np
import iopro

import gc

seshness

The Shared Repository Model

$ git clone [email protected]:berkeley-food-recommendations/data-gathering.git

You're cloning the main repository - be careful! We're going to enforce a "no committing to master directly" rule, so no committing directly to master, please.

Short Version

fnielsen

#!/usr/bin/python 
#
# (originally entered at https://gist.github.com/1035399)
#
# License: GPLv3
#
# To download the AFINN word list do:
# wget http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/6010/zip/imm6010.zip
# unzip imm6010.zip
#

ndarville

Business Models

Advertising

Models	Examples
Display ads	Yahoo!
Search ads	Google

shlomibabluki

public class Maze {

  public int counter = 0;
	
	public char[][] maze =
		   {{'#', '#', '#', '#', '#', '#', '#', '#', '#', '#'},
			{'#', ' ', ' ', ' ', '#', ' ', '#', ' ', ' ', '#'},
			{'#', ' ', ' ', ' ', '#', ' ', '#', ' ', '#', '#'},
			{'#', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '#'},
			{'#', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '#'},

shlomibabluki

public class Maze_Best {

public int counter = 0;
private final static int MAX_VALUE = 1000;	
	
	int best_solution = MAX_VALUE;
	
	public char[][] maze =
		   {{'#', '#', '#', '#', '#', '#', '#', '#', '#', '#'},
			{'#', ' ', ' ', ' ', '#', ' ', '#', ' ', ' ', '#'},

bradmontgomery

"""
Use a Counter to find the most common words in "The Wonderful Wizard of Oz" by
L. Frank Baum.

Available in (mostly) plain text at:
https://archive.org/stream/wonderfulwizardo00baumiala/wonderfulwizardo00baumiala_djvu.txt

Note: This code also counts the words in the header, so it's not a *realistic*
applicaton, but more of a demonstration of python's Counter.

glamp

import numpy as np
import pandas as pd
import pylab as pl

baseball = pd.read_csv("http://bit.ly/144sh7t")

# group by year and get a summary of each numeric column
baseball.groupby(["year"]).describe()
# for each year, get the mean of each column
baseball.groupby(["year"]).aggregate(np.mean)

glamp

from dateutil.parser import parse
import pandas as pd

# monthly slaughter records since 1921
df = pd.read_csv("http://bit.ly/119792b")
# parse the data (we could also use pd.to_datetime)
df.date = df.date.apply(parse)
# sort the data frame by date
df = df.sort(['date'])
# create an index

damianavila

"""
Usage: python remove_output.py notebook.ipynb [ > without_output.ipynb ]
Modified from remove_output by Minrk

"""
import sys
import io
import os
from IPython.nbformat.current import read, write